Technische Universität Berlin, Faculty IV - Electrical Engineering and Computer Science, the Institute for Software Engineering and Theoretical Computer Science and the Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, are looking for applications for joint appointment within the framework of the reimbursement model (Berlin Model) for a period of five years.
for the chair "Machine Learning and Communication“
associated with the head of the "Machine Learning" department of the Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI.
Technische Universität Berlin is one of the largest, internationally renowned and traditional technical universities in Germany. Its efforts to increase knowledge and technological progress are based on the principles of excellence and quality.
Together with the Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI, the Technische Universität Berlin conducts applied research and development in the future field of machine learning and communication.
The Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI is a world leader in the research of mobile and optical communication networks and systems and thus contributes significantly to the standards for information and communication technologies. Fraunhofer HHI researches the entire spectrum of digital infrastructure, from measurement and representation to transport and evaluation of signals.
Supported by a constantly growing number of available training data and suitable computer architectures, machine learning (ML) is increasingly reaching the potential of human performance and has already become an industrial standard in some areas such as image, text and speech processing. Machine learning is also used in the field of mobile networks for a variety of optimization methods. In this area of application, machine learning will in all likelihood have a formative influence in the future and will raise completely new research questions of its own.
In your role as Professor for Machine Learning and Communication, you will explore the theoretical and methodological fundamentals of machine learning. You will further develop existing methods (e.g. deep learning methods) as well as new models and architectures adapted to the respective (communication) application (distributed learning, edge computing, image and video communication). Practically relevant characteristics such as reliability, efficiency and transparency of these methods are the focus of the research activities.
You will bring with you experience with deep learning methods and their application in signal processing and communication as well as with interdisciplinary cooperation in this field. Excellent research achievements and teaching experience in the areas of decentralized machine learning, interpretability and compression of ML models and the application of ML in mobile communication and image and video communication are expected.
The teaching obligation at the Technische Universität Berlin is 2 SWS.
In your role as head of the "Machine Learning" working group at Fraunhofer HHI, you will be responsible for the scientific, technical and entrepreneurial control and development of the group within the Fraunhofer model and the Fraunhofer overall strategy. Experience in the strategic planning, acquisition and implementation of national and international research and development projects as well as competencies to increase the efficiency of development processes and in technology exploitation are advantageous.
You should be able to competently represent the main topics in research and teaching as well as in research and technology management vis-à-vis research sponsors and research partners and to expand the strategic link between the university and the Fraunhofer Institute.
Fulfilment of the requirements for appointment according to § 100 Berlin Higher Education Act. This includes in particular a completed university degree, qualified achievements in research (generally proven by PhD), additional research archievements (Habiliation, post-doctoral teaching, or equivalent qualification) as well as pedagogical suitability, represented or proven by a teaching portfolio (more information on this on the TUB website, direct access 144242).
Non-German-speaking applicants are expected to commit themselves to learning the German language quickly. Good knowledge of English is desirable.
How to apply:
The Technische Universität Berlin aims to increase the proportion of women in research and teaching and therefore expressly invites qualified female scientists to apply. Severely disabled applicants will be given preferential consideration if they are equally qualified. The TU Berlin appreciates the diversity of its members and pursues the goals of equal opportunities.
We are certified as a family-friendly university. The Technische Universität Berlin and the Fraunhofer-Gesellschaft pursue a family-friendly personnel policy and offer their employees flexible working hours and support to reconcile work and family life. In addition, the Dual Career Service of the Technical University of Berlin provides active assistance to newly-appointed people moving with their entire family. Applications from abroad are explicitly welcome.
Please send your application until May 29, 2020 indicating the reference number and including the appropriate documentation (including a CV listing publications, teaching experience etc., copies of academic degrees, teaching portfolio and draft of prospective teaching and research projects, as well as copies of up to five selected publications) only in digital format by e-mail to firstname.lastname@example.org : Technische Universität Berlin – Der Präsident – Dekan der Fakultät IV, Sekr. MAR 6-1, Marchstr. 23, 10587 Berlin.
By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at TU website quick access 214041.
including a CV listing publications, teaching experience etc., copies of academic degrees, teaching portfolio and draft of prospective teaching and research projects, as well as copies of up to five selected publications